# Importing Libraries and Data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import altair as alt
import mplfinance as mpf
import plotly.graph_objects as go


company_df = pd.read_csv('../data/sp500_companies.csv')
stock_df = pd.read_csv('../data/sp500_stocks.csv')

# Create new columns with Moving Averages and Standard Deviations

stock_df['Date'] = pd.to_datetime(stock_df['Date'])

stock_df['MA_10'] = stock_df.groupby('Symbol')['Close'].rolling(window=10).mean().reset_index(level=0, drop=True)
stock_df['MA_20'] = stock_df.groupby('Symbol')['Close'].rolling(window=20).mean().reset_index(level=0, drop=True)
stock_df['MA_50'] = stock_df.groupby('Symbol')['Close'].rolling(window=50).mean().reset_index(level=0, drop=True)
stock_df['MA_200'] = stock_df.groupby('Symbol')['Close'].rolling(window=200).mean().reset_index(level=0, drop=True)
stock_df['std_10'] = stock_df.groupby('Symbol')['Close'].rolling(window=10).std().reset_index(level=0, drop=True)
stock_df['std_20'] = stock_df.groupby('Symbol')['Close'].rolling(window=20).std().reset_index(level=0, drop=True)
stock_df['std_50'] = stock_df.groupby('Symbol')['Close'].rolling(window=50).std().reset_index(level=0, drop=True)
stock_df['std_200'] = stock_df.groupby('Symbol')['Close'].rolling(window=200).std().reset_index(level=0, drop=True)

# Create new columns with Bollinger Bands for each Moving Average

stock_df['upper_band_10'] = stock_df['MA_10'] + (stock_df['std_10'] * 2)
stock_df['lower_band_10'] = stock_df['MA_10'] - (stock_df['std_10'] * 2)

stock_df['upper_band_20'] = stock_df['MA_20'] + (stock_df['std_20'] * 2)
stock_df['lower_band_20'] = stock_df['MA_20'] - (stock_df['std_20'] * 2)

stock_df['upper_band_50'] = stock_df['MA_50'] + (stock_df['std_50'] * 2)
stock_df['lower_band_50'] = stock_df['MA_50'] - (stock_df['std_50'] * 2)


stock_df['upper_band_200'] = stock_df['MA_200'] + (stock_df['std_200'] * 2)
stock_df['lower_band_200'] = stock_df['MA_200'] - (stock_df['std_200'] * 2)

# Create new columns Indicating Golden Cross and Death Cross

stock_df['Golden_Cross_Short'] = np.where((stock_df['MA_10'] > stock_df['MA_20']) & (stock_df['MA_10'].shift(1) <= stock_df['MA_20'].shift(1)), 1, 0)
stock_df['Golden_Cross_Medium'] = np.where((stock_df['MA_20'] > stock_df['MA_50']) & (stock_df['MA_20'].shift(1) <= stock_df['MA_50'].shift(1)), 1, 0)
stock_df['Golden_Cross_Long'] = np.where((stock_df['MA_50'] > stock_df['MA_200']) & (stock_df['MA_50'].shift(1) <= stock_df['MA_200'].shift(1)), 1, 0)

stock_df['Death_Cross_Short'] = np.where((stock_df['MA_10'] < stock_df['MA_20']) & (stock_df['MA_10'].shift(1) >= stock_df['MA_20'].shift(1)), 1, 0)
stock_df['Death_Cross_Medium'] = np.where((stock_df['MA_20'] < stock_df['MA_50']) & (stock_df['MA_20'].shift(1) >= stock_df['MA_50'].shift(1)), 1, 0)
stock_df['Death_Cross_Long'] = np.where((stock_df['MA_50'] < stock_df['MA_200']) & (stock_df['MA_50'].shift(1) >= stock_df['MA_200'].shift(1)), 1, 0)

# Create new columns for MACD, Signal Line and MACD Histogram

def calculate_macd(df, short_window=12, long_window=26, signal_window=9):
    # Calculate the short-term EMA
    df['EMA_short'] = df['Close'].ewm(span=short_window, adjust=False).mean()
    
    # Calculate the long-term EMA
    df['EMA_long'] = df['Close'].ewm(span=long_window, adjust=False).mean()
    
    # Calculate the MACD line
    df['MACD'] = df['EMA_short'] - df['EMA_long']
    
    # Calculate the Signal line
    df['Signal'] = df['MACD'].ewm(span=signal_window, adjust=False).mean()
    
    # Calculate the MACD histogram
    df['MACD_Hist'] = df['MACD'] - df['Signal']
    
    return df

stock_df = calculate_macd(stock_df)

# Create new columns for Average True Range (ATR) and True Range (TR)

stock_df['Previous_Close'] = stock_df['Close'].shift(1)

# True Range, Shows the volatility of the stock
stock_df['TR'] = stock_df.apply(
    lambda row: max(
        row['High'] - row['Low'],  # High - Low
        abs(row['High'] - row['Previous_Close']),  # |High - Previous Close|
        abs(row['Low'] - row['Previous_Close'])  # |Low - Previous Close|
    ), axis=1
)

# Average True Range, Shows the average volatility of the stock
stock_df['ATR'] = stock_df['TR'].rolling(window=10).mean()

# Create new columns for Relative Strength Index (RSI)

def calculate_rsi(df, window=10):
    # Calculate daily price changes
    delta = df['Close'].diff()

    # Separate gains and losses
    gain = delta.where(delta > 0, 0)
    loss = -delta.where(delta < 0, 0)

    # Calculate the average gain and average loss
    avg_gain = gain.rolling(window=window, min_periods=1).mean()
    avg_loss = loss.rolling(window=window, min_periods=1).mean()

    # Calculate the Relative Strength (RS)
    rs = avg_gain / avg_loss

    # Calculate the RSI
    rsi = 100 - (100 / (1 + rs))

    return rsi

# Add RSI column to the DataFrame
stock_df['RSI_10_Day'] = calculate_rsi(stock_df)

# Create new columns for 10 Day Rate of Change (ROC)
stock_df['10_Day_ROC'] = ((stock_df['Close'] - stock_df['Close'].shift(10)) / stock_df['Close'].shift(10)) * 100

# Create new columns for 10,20,50 day resistance and support levels
stock_df['Resistance_10_Day'] = stock_df['Close'].rolling(window=10).max()
stock_df['Support_10_Day'] = stock_df['Close'].rolling(window=10).min()
stock_df['Resistance_20_Day'] = stock_df['Close'].rolling(window=20).max()
stock_df['Support_20_Day'] = stock_df['Close'].rolling(window=20).min()
stock_df['Resistance_50_Day'] = stock_df['Close'].rolling(window=50).max()
stock_df['Support_50_Day'] = stock_df['Close'].rolling(window=50).min()

# Create new columns for 10,20,50 day Volume Indicators
stock_df['Volume_MA_10'] = stock_df['Volume'].rolling(window=10).mean()
stock_df['Volume_MA_20'] = stock_df['Volume'].rolling(window=20).mean()
stock_df['Volume_MA_50'] = stock_df['Volume'].rolling(window=50).mean()

# Create new columns for On Balance Volume (OBV)

def create_OBV(df):
    df['OBV'] = 0
    for i in range(1, len(df)):
        if df['Close'].iloc[i] > df['Close'].iloc[i - 1]:
            df.loc[df.index[i], 'OBV'] = df['OBV'].iloc[i - 1] + df['Volume'].iloc[i]
        elif df['Close'].iloc[i] < df['Close'].iloc[i - 1]:
            df.loc[df.index[i], 'OBV'] = df['OBV'].iloc[i - 1] - df['Volume'].iloc[i]
        else:
            df.loc[df.index[i], 'OBV'] = df['OBV'].iloc[i - 1]
    return df

# stock_df = create_OBV(stock_df)

# Preview Data

stock_df['Z-score'] = (stock_df['Close'] - stock_df['Close'].mean()) / stock_df['Close'].std()
stock_df['Daily Return'] = stock_df.groupby('Symbol')['Close'].pct_change(fill_method=None).reset_index(level=0, drop=True)
company_df.fillna(0, inplace=True)
stock_df.fillna(0, inplace=True)
stock_df

alt.data_transformers.disable_max_rows()
def plot_closing_price(df, company,opacity=1):
    filtered_df = df[df['Symbol'] == company]
    chart = alt.Chart(filtered_df).mark_line(color='black', opacity=opacity).encode(
        alt.X('Date:T', title='Date'),
        alt.Y('Close:Q', title='Closing Price'),
        alt.Tooltip(['Date:T', 'Close:Q', 'Volume:Q','RSI_10_Day']),
    ).properties(
        title=f'{company} Closing Price',
        width=800,
        height=400
    ).interactive(
        bind_y=False
    )
    return chart
plot_closing_price(stock_df, 'INTC')

def plot_candlestick(df, company,window:list = None,SR_window=10):
    # Fileter data to specified stock and date range
    filtered_df = df[df['Symbol'] == company]
    filtered_df = filtered_df[filtered_df['Date'] >= window[0]]
    filtered_df = filtered_df[filtered_df['Date'] <= window[1]]
    filtered_df['Date'] = pd.to_datetime(filtered_df['Date'])

    df = filtered_df

    # Create the Candlesick Chart
    candlestick = go.Candlestick(
        x=df['Date'],
        open=df['Open'],
        high=df['High'],
        low=df['Low'],
        close=df['Close'],
        name='Candlestick'
        )
    
    # Create the Volume Bar graph
    volume = go.Bar(
        x=df['Date'],
        y=df['Volume'],
        name='Volume',
        marker_color='blue',
        opacity=0.5,
        yaxis='y2'
    )

    # Create the Support and Resistance Lines
    support = go.Scatter(
        x=df['Date'],
        y=df[f'Support_{SR_window}_Day'],
        mode='lines',
        name=f'Support {SR_window} Day',
        line=dict(color='green', width=1)
    )

    resistance = go.Scatter(
        x=df['Date'],
        y=df[f'Resistance_{SR_window}_Day'],
        mode='lines',
        name=f'Resistance {SR_window} Day',
        line=dict(color='red', width=1)
    )

    # Create the Layout for the Chart (Title, Axis Labels, etc.)
    layout = go.Layout(
        title= f'{company} Candlestick Chart From {window[0]} to {window[1]}',
        xaxis=dict(title='Date'),
        yaxis=dict(title='Price', showgrid=True),
        yaxis2=dict(title='Volume', overlaying='y', side='right', showgrid=False),
        xaxis_rangeslider_visible=False,
        hovermode='x unified', # Compare data points on hover
        plot_bgcolor='white'
    )

    # Layer all the charts together into one figure
    fig = go.Figure(
        data=[candlestick, volume, support, resistance],
        layout=layout
        )
    return fig
plot_candlestick(stock_df, 'INTC', window=['2024-06-01', '2024-10-01'],SR_window=20)

def plot_moving_average(df, company, color, MA,term):
    filtered_df = df[df['Symbol'] == company]
    chart = alt.Chart(filtered_df).mark_line(color=color).encode(
        alt.X('Date:T', title='Date'),
        alt.Y(f'{MA}:Q', title='{MA}'),
        alt.Tooltip([MA,'Date', 'Close:Q', 'Volume:Q', 'Death_Cross_Short:O', 'Golden_Cross_Short:O', 'Death_Cross_Long:O', 'Golden_Cross_Long:O'])
    ).properties(
        title=f'{company} Moving Average {term}',
        width=800,
        height=400
    ).interactive(
        bind_y=False
    )

    
    return chart
closing_price = plot_closing_price(stock_df, 'INTC', 0.5)
MA_10 = plot_moving_average(stock_df, 'INTC', 'green', 'MA_10','Short Term')
MA_20 = plot_moving_average(stock_df, 'INTC', 'blue', 'MA_20','Short Term')

MA_50 = plot_moving_average(stock_df, 'INTC', 'red', 'MA_50','Long Term')
MA_200 = plot_moving_average(stock_df, 'INTC', 'purple', 'MA_200','Long Term')
short_term_MA = alt.layer(MA_10, MA_20, closing_price)
medium_term_MA = alt.layer(MA_200, MA_50, closing_price)
MA_charts = alt.vconcat(short_term_MA, medium_term_MA)
MA_charts

def plot_bollinger_bands(df, company, band):
    # Filter data to specified stock and date range
    df = df[df['Symbol'] == company]
    window=['2023-01-01', '2024-10-01']
    df = df[df['Date'] >= window[0]]
    df = df[df['Date'] <= window[1]]
    fig = go.Figure()

    # Create the Close Price Line Chart
    fig.add_trace(
        go.Scatter(
            x=df['Date'],
            y=df['Close'],
            mode='lines',
            name='Close Price'
        )
    )

    # Create the upper band
    fig.add_trace(
        go.Scatter(
            x=df['Date'],
            y=df[f'upper_band_{band}'],
            mode='lines',
            name='Upper Band',
            line=dict(color='red', width=1)
        )
    )

    # Create the lower band
    fig.add_trace(
        go.Scatter(
            x=df['Date'],
            y=df[f'lower_band_{band}'],
            mode='lines',
            fill='tonexty',
            fillcolor='rgba(128, 128, 128, 0.2)',
            name='Lower Band',
            line=dict(color='red', width=1)
        )
    )

    # Update layout
    fig.update_layout(
        title=f'Bollinger Bands for {band} Day Moving Average',
        xaxis_title='Date',
        yaxis_title='Price',
        legend_title='Legend',
        width=1000,
        height=600
    )

    fig.show()

band_10 = plot_bollinger_bands(stock_df, 'INTC', 10)
band_20 = plot_bollinger_bands(stock_df, 'INTC', 20)
band_50 = plot_bollinger_bands(stock_df, 'INTC', 50)
# band_200 = plot_bollinger_bands(stock_df, 'INTC', 200)

def plot_RSI(df, company,start):
    filtered_df = df[df['Symbol'] == company]
    filtered_df = filtered_df[filtered_df['Date'] >= start]
    fig, axs = plt.subplots(2, 1, gridspec_kw={'height_ratios': [3, 1]}, figsize=(10, 6))
    
    axs[0].set_title(f'{company} Closing Price')
    axs[0].plot(filtered_df['Date'],filtered_df['Close'], color='black')
    axs[1].axhline(y=70, color='red', linestyle='--')
    axs[1].axhline(y=30, color='green', linestyle='--')
    axs[1].plot(filtered_df['Date'],filtered_df['RSI_10_Day'], color='orange')
    axs[1].set_title('RSI')
    plt.show()

plot_RSI(stock_df, 'INTC', '2024-06-01')

from plotly.subplots import make_subplots

def plot_MACD(df, company, start, stop):
    
    # Filter the DataFrame for the specified company and date range
    df = df[(df['Symbol'] == company)] 
    df = df[df['Date'] >= start]
    df = df[df['Date'] <= stop]
    
    # Create a Plotly figure with two subplots
    fig = make_subplots(
        rows=2, 
        cols=1, 
        shared_xaxes=True, 
        vertical_spacing=0.1, 
        subplot_titles=('Close Price', 'MACD')
        )
    
    # Add Close Price graph
    fig.add_trace(go.Scatter(
        x=df['Date'], y=df['Close'],
        mode='lines',
        name='Close Price'
    ), row=1, col=1)
    
    # Add MACD line graph
    fig.add_trace(go.Scatter(
        x=df['Date'], y=df['MACD'],
        mode='lines',
        name='MACD',
        line=dict(color='red')
    ), row=2, col=1)
    
    # Add Signal line graph
    fig.add_trace(go.Scatter(
        x=df['Date'], y=df['Signal'],
        mode='lines',
        name='Signal',
        line=dict(color='blue')
    ), row=2, col=1)
    
    # Add MACD histogram 
    fig.add_trace(go.Bar(
        x=df['Date'], y=df['MACD_Hist'],
        name='MACD Histogram',
        marker_color=['green' if x > 0 else 'red' for x in df['MACD_Hist']]
    ), row=2, col=1)
    
    # Update layout
    fig.update_layout(
        title=f'MACD for {company}',
        xaxis_title='Date',
        yaxis_title='Price',
        legend_title='Legend',
        width=1000,
        height=600
    )
    
    fig.show()
start = pd.to_datetime('01-01-2024')
stop = pd.to_datetime('09-29-2024')
MACD = plot_MACD(stock_df, 'INTC', start, stop)

	Date	Symbol	Adj Close	Close	High	Low	Open	Volume	MA_10	MA_20	...	Support_10_Day	Resistance_20_Day	Support_20_Day	Resistance_50_Day	Support_50_Day	Volume_MA_10	Volume_MA_20	Volume_MA_50	Z-score	Daily Return
0	2010-01-04	A	20.434929	22.389128	22.625179	22.267525	22.453505	3815561.0	0.000000	0.00000	...	0.000	0.000000	0.000	0.000000	0.000	0.0	0.00	0.00	-0.384398	0.000000
1	2010-01-05	A	20.212959	22.145924	22.331903	22.002861	22.324751	4186031.0	0.000000	0.00000	...	0.000	0.000000	0.000	0.000000	0.000	0.0	0.00	0.00	-0.385545	-0.010863
2	2010-01-06	A	20.141132	22.067240	22.174536	22.002861	22.067240	3243779.0	0.000000	0.00000	...	0.000	0.000000	0.000	0.000000	0.000	0.0	0.00	0.00	-0.385915	-0.003553
3	2010-01-07	A	20.115025	22.038628	22.045780	21.816881	22.017166	3095172.0	0.000000	0.00000	...	0.000	0.000000	0.000	0.000000	0.000	0.0	0.00	0.00	-0.386050	-0.001297
4	2010-01-08	A	20.108498	22.031473	22.067240	21.745352	21.917025	3733918.0	0.000000	0.00000	...	0.000	0.000000	0.000	0.000000	0.000	0.0	0.00	0.00	-0.386084	-0.000325
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1875709	2024-10-28	LYV	118.430000	118.430000	119.389999	116.904999	117.160004	1786063.0	116.653001	115.03400	...	0.389	233.399994	0.389	749.119995	0.389	18070329.9	16995342.65	12071518.84	0.068235	0.000000
1875710	2024-10-28	INTC	22.920000	22.920000	23.270000	22.559999	22.684999	38933999.0	22.627160	22.78258	...	0.389	233.399994	0.389	749.119995	0.389	18490356.5	18852739.45	12847144.68	-0.381896	0.000000
1875711	2024-10-28	DNMR	0.389000	0.389000	0.395600	0.370000	0.403000	1270538.0	0.000000	0.00000	...	0.389	233.399994	0.389	749.119995	0.389	17647780.5	17179579.70	12845380.04	-0.488083	0.000000
1875712	2024-10-28	SPCE	7.740000	7.740000	7.870000	7.142600	7.210000	2533542.0	0.000000	0.00000	...	0.389	233.399994	0.389	749.119995	0.389	15497806.0	16821441.90	12892494.20	-0.453439	0.000000
1875713	2024-10-29	INTC	22.705000	22.705000	23.139999	22.410000	22.870001	43573446.0	22.620660	22.78833	...	0.389	233.399994	0.389	749.119995	0.389	19676544.3	17798449.85	13718152.06	-0.382910	-0.009380

Technical Analysis Of Intel¶

Purpose¶

1.0 Load Data and Calculate Key Statistics¶

Potential stocks of interest¶

Preview Data with Additional Columns¶

Column Descriptions¶

Visualize Data¶

Line Chart of Closing Price¶

CandleStick Chart¶

Bollinger Bands¶

Relative Strength Index¶